gbm (2.1.3)

3 users

Generalized Boosted Regression Models.

An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).

Maintainer: ORPHANED
Author(s): Greg Ridgeway <> with contributions from others

License: GPL (>= 2) | file LICENSE

Uses: lattice, survival, RUnit
Reverse depends: BigTSP, biomod2, bst, bujar, CompModSA, ecospat, gbm2sas, imputation, mma, ModelMap, mseq, personalized, soil.spec, twang
Reverse suggests: AzureML, BiodiversityR, biomod2, caret, caretEnsemble, crimelinkage, DALEX, dismo, featurefinder, fscaret, mboost, mlr, modelcf, ModelMap, opera, pdp, plotmo, pmml, preprosim, soil.spec, SuperLearner

Released about 1 year ago.

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Visit gbm on R Graphical Manual.